The instant invention relates to an imaging-based biomarker for characterizing the structure or function of human or animal brain tissue and to two methods for characterizing the structure or function of human or animal brain tissue by using such a biomarker.
Biomarkers, especially biomarkers derived from magnetic resonance (MR), positron emission tomography (PET) or magnetic particle images, allow detection and quantitative characterization of structural or functional alterations of the human or animal brain which can occur in association with various diseases including, but not restricted to cerebrovascular, neurodegenerative, and inflammatory diseases. By this, biomarkers based on these imaging modalities may support diagnosis, therapy planning and therapy monitoring in clinical routine patient care. Such biomarkers may also play an important role in the development of new treatments, new drugs and non-pharmacological treatment options, not only by supporting the inclusion of appropriate patients into clinical trials but also by providing objective outcome measures for the evaluation of therapy effects.
Biomarkers contribute to improved accuracy of a diagnosis compared to conventional clinical diagnosis using only symptom-based criteria. This is achieved by providing evidence of the patho-physiological changes in the brain characteristic for the underlying disease.
Cerebrovascular diseases' is the umbrella term for diseases that affect blood vessels supplying and draining the brain. Cerebrovascular diseases can affect small and/or large vessels. Cerebrovascular disease can be detected in MR images of the brain in which it manifests with a variety of different structural lesions including (but not restricted to) large infarcts, recent small subcortical infarcts, lacunes, subcortical hyperintensities, perivascular spaces, microbleeds, and brain atrophy [1].
MR imaging generally allows detection and quantitative characterization of structural lesions in the human or animal brain including (but not restricted to) those associated with cerebrovascular disease.
For example, subcortical hyperintensities are per definition present as hyperintensities in T2-weighted MR images and are located within the brain's white matter or in subcortical grey matter or in the brainstem. Thus, subcortical hyperintensities are lesions (within the specified brain regions) that appear brighter than normal in T2-weighted MR images. They can be easily detected by visual inspection of T2-weighted MR images (see FIG. 1A).
Structural lesions in the brain very commonly occur in older age so that virtually all elderly people show structural brain lesions, although to a strongly variable extent. Structural brain lesions can be associated with the whole spectrum of cognitive decline/dysfunction, ranging from subjective cognitive decline over mild cognitive impairment to dementia affecting activities of daily living. However, structural brain lesions can be present also without causing any symptoms. Thus, it is an important diagnostic problem to decide whether the structural brain lesions detected in a given patient are the cause of his cognitive decline or not. In the latter case, the patient should be referred to further diagnostic tests in order to identify the underlying disease, for example Alzheimer's disease.
The reliable detection of the cause of cognitive decline, for example the differentiation between vascular cognitive decline and Alzheimer's disease, has immediate therapeutic consequences: reducing risk factors in order to avoid progression in vascular cognitive decline versus cholinesterase inhibitors in Alzheimer's disease. Another, clinically highly relevant diagnostic problem is the estimation of the risk associated with detected structural brain lesions, for example the risk of cognitive decline or the risk of stroke in the future.
There is increasing evidence in the scientific literature that the pattern of the brain lesion load provides information that is relevant to both questions, i.e. differential diagnosis and risk stratification.
Nevertheless, in clinical routine patient care brain lesion load is usually assessed only qualitatively or using a visual scoring system [2]. However, these visual scores have been shown to be quite variable not only between different raters (low inter-rater stability) but also when the same rater repeats the scoring of the same image (low intra-rater stability). This clearly limits the usefulness of these visual scores.
Quantitative assessment of structural brain lesions was previously performed by manual lesion delineation, by automatic lesion segmentation algorithms or by a combination of both [3-8]. Most of the described semi-automatic software tools provide the option for localization of detected lesions, both on the basis of brain regions predefined in an anatomical standard (atlas) space or by using parcellation techniques.
It is further known from prior art to define an ‘asphericity’ of a tumor in whole body positron emission tomography (PET) with the glucose analog [F-18]-fluorodeoxyglucose (FDG) [9-11]. The asphericity in FDG PET is a measure of the shape irregularity of the metabolically active part of the tumor and has been proposed to predict the survival time of tumor patients. The asphericity is applied to a single tumor lesion. It has not been applied to several lesions or lesion patterns. As a consequence, the asphericity of a tumor has never been weighted in any way.
Positron emission tomography of the brain with the glucose analog F-18-fluorodeoxyglucose (FDG PET) provides biomarkers for altered (synaptic) brain function. Alterations of brain function can be caused by loss/dysfunction of neurons indicative of a neurodegenerative disease, e.g. Alzheimer's disease (AD).
U.S. Pat. No. 6,366,797 B1 describes a method of analyzing magnetic resonance images of a brain to determine the severity of a medical condition by calculating a ratio between the brain volume and volume of a specific area within the brain.
U.S. Pat. No. 7,995,825 B2 describes a method of classifying tissue in a magnetic resonance image by constructing a pixel intensity histogram of a previously acquired magnetic resonance image and applying a statistical regression analysis to the histogram to determine a pixel intensity threshold value for segmenting the histogram into at least two regions.
US 2003/0088177 A1 describes a method for assessing a neurological condition of a patient by identifying a biomarker of the nervous system of the patient in a three-dimensional image and by storing an identification of the biomarker and a quantitative measurement thereof in a storage medium. The biomarker can be a shape, topology, and morphology of brain lesions, of brain plaques, of brain ischemia, or of brain tumors; a spatial frequency distribution of sulci and gyri; a compactness of grey matter and white matter; whole brain characteristics; grey matter characteristics; white matter characteristics; cerebral spinal fluid characteristics; hippocampus characteristics; brain sub-structure characteristics; a ratio of cerebral spinal fluid volume to grey matter and white matter volume; and a number and volume of brain lesions.
U.S. Pat. No. 8,112,144 B2 describes a cerebral atrophy assessment device that is arranged and designed to calculate a numerical value representing a volume of a convex hull of the grey matter or the white matter of a brain, and to calculate a value of a first ratio between this numerical value and a numerical value representing the brain volume. Afterwards, a cerebral atrophy is assessed from the value of the first ratio.
U.S. Pat. No. 8,423,118 B2 describes a system for automated differential diagnosis of dementia, including a knowledge base that comprises a plurality of brain scan images exhibiting patterns of a plurality of types and degrees of dementia and one or more healthy brain scan images, wherein diagnosis information can be output by the system that includes an image of the patient's brain scan image with highlighted hypo-metabolic regions, wherein the highlighting is color-coded to indicate a type of dementia, wherein different colors correspond to different types of dementia.
The impact of structural brain lesions including white matter hyperintensities (WMHs) on cerebral glucose metabolism is well-documented in the literature. Kochunov et al (2009) [12] documented the association between WMH burden and global reduced cerebral glucose metabolism. Tullberg et al. (2004) [13] and Reed et al. (2004) [14] found a strong association between WMHs and a regional decline in cerebral glucose metabolism most pronounced in the frontal lobes. A recent work by Glodzik et al. (2014) [15] demonstrated that disruption of white matter tracts connecting grey matter regions caused by structural brain lesions results in a decline in glucose metabolism in connected grey matter regions.